An aircraft is equipped with a large number of sensors making it possible to measure its flight parameters (speed, attitude, position, altitude, etc.) and more generally its state at each instant.
These flight parameters are thereafter used by avionics systems, in particular the automatic piloting system, the flight computers (Flight Control Computer Systems), the aircraft control and guidance system (Flight Guidance System), which systems are among the most critical of the aircraft. Because of the criticality of these systems, the measured parameters must exhibit high integrity and high availability. Integrity is understood to mean that the parameter values used by the avionics systems are not erroneous because of any fault. Availability is understood to mean that the sensors providing these parameters must be sufficiently redundant for it to be possible for a measurement of each parameter to be permanently available. If a sensor develops a fault and cannot provide any measurements, another sensor takes over.
Generally, an avionics system receives measurements of one and the same parameter originating from several redundant sensors. When these measurements differ, the system performs a processing (data fusion) so as to estimate, by consolidation of the measurements, the parameter with the lowest risk of error. This processing is generally an average or a median of the measurements in question.
FIG. 1 illustrates a first exemplary processing of the measurements provided by redundant sensors.
Represented in this figure are the measurements of one and the same flight parameter, performed respectively by three sensors, A1, A2, A3, as a function of time.
The processing consists here in calculating at each instant the median value of the measurements. Thus, in the example illustrated we select the measurement a2(t) of A2 up to time t1 and, beyond, the measurement a1(t) of A1. Also represented in the chart is a tolerance band, of width 2Δ around the median. If one of the measurements falls outside the tolerance band (for example the measurement a2(t) reckoning from the time t2), it is considered that this measurement is erroneous and the latter is no longer taken into account in the estimation of the parameter in question. The corresponding sensor (here A2) is disabled for the rest of the processing.
Generally, this processing can be applied provided that the number of redundant sensors is odd.
When the number of sensors is even, or else the number of sensors is odd but a sensor has already been disabled, the values of the measurements are simply averaged at each instant to obtain an estimation of the parameter in question.
All the sensors pertaining to one and the same technology may be affected by a common fault (for example presence of ice in the Pitot tubes, static pressure taps blocked, angle of incidence probes frozen, fault with one and the same electronic component, etc.). In this case, the aforementioned processing methods are not capable of identifying the erroneous sources. It is then advantageous to call upon additional sensors implementing a different technology or technologies. Hence, several sets of sensors are generally employed, making it possible to measure one and the same parameter, the technologies of the various sets of sensors being chosen dissimilar. By dissimilar technologies is meant that these technologies use different physical principles or different implementations.
For example, it is possible to resort to a first set of sensors capable of measuring the speed of the aircraft on the basis of pressure probes (total pressure and static pressure), also dubbed ADRs (Air Data Reference units), to a first estimator capable of estimating the speed as a function of the angle of incidence and of the lift (by the lift equation), and to a second estimator capable of estimating this speed on the basis of the engine data.
A first approach for estimating the parameter is to fuse all the measurements taken by the sensors, dissimilar or not, according to the same principle as previously. For example at a given instant, the median or the average of the values measured by the various sensors will be taken.
This first approach improves the robustness of the estimation of the parameter by liberating it of the faults that may affect a particular technology. On the other hand, it may lead to a noticeable reduction in the accuracy of the estimation as illustrated with the aid of the example hereinafter.
FIG. 2A represents the values of a flight parameter (here the speed of the aircraft) measured by a first set of three sensors (denoted A1,A2,A3) using a first technology and by a second set of two sensors (denoted B1,B2) using a second technology dissimilar to the first. The measurements are denoted respectively a1(t),a2(t),a3(t) for the first set of sensors and b1(t),b2(t) for the second set of sensors. It is assumed that the measurements a1(t), a2(t), a3(t) are substantially more accurate than the measurements b1(t),b2(t). The real speed of the aircraft has been represented by V(t).
It is assumed that at the time tf the sensors A1 and A2 of the first set are affected by one and the same fault. As may be seen in the figure, onwards of the time tf, the measurements a1(t), a2(t) drift and deviate substantially from the real value of the parameter, V(t).
FIG. 2B represents the estimation {circumflex over (V)}(t) of the speed obtained as the median of the measurements a1(t), a2(t), a3(t),b1(t),b2(t). It is seen that onwards of the time tc, the calculation of the median amounts to selecting the measurement b2(t) of the sensor B2. Now, this measurement is much less accurate than the measurement a3(t) of the sensor A3 which is nevertheless available and valid.
It is seen that the data fusion applied to the whole set of measurements leads here to sub-optimal estimation accuracy.
The aim of the present disclosure is consequently to propose a data fusion method capable of fusing the measurements of a parameter, for example an aircraft flight parameter, that are taken by a plurality of sensors, of different technologies and accuracies, so as to obtain an estimation of this parameter which is not only available and robust but which also exhibits better accuracy than in the prior art.